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Distributed energy trading for an integrated energy system and electric vehicle charging stations: A Nash bargaining game approach

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  • Wang, Yifei
  • Wang, Xiuli
  • Shao, Chengcheng
  • Gong, Naiwei

Abstract

The increasing concerns of energy utilization and climate change have promoted the permeation of various smart energy subsystems on the distribution level, such as integrated energy systems (IESs) and electric vehicle charging stations (EVCSs). These subsystems typically act separately during operation and their transaction values have not yet been well investigated. In this paper, we propose an energy trading model based on the Nash bargaining game to study cooperative benefits between an IES and several EVCSs. The proposed model not only considers individual interests, but also enables the players to fairly benefit from cooperation. In particular, the uncertainties of the market prices, renewable energies and integrated demand response are considered. To ensure that the entire game is computationally tractable, the original problem is decomposed into a major energy trading problem and an additional payment bargaining problem. Furthermore, a distributed algorithm based on modified Benders decomposition is used to overcoming the players’ privacies. The results show the considerable benefits where the costs of the IES may be reduced by 3.89% and the profits associated with the EVCSs may be increased by at least 7.8%. The proposed algorithm is proven to be able to find the optimal global solutions efficiently and accurately.

Suggested Citation

  • Wang, Yifei & Wang, Xiuli & Shao, Chengcheng & Gong, Naiwei, 2020. "Distributed energy trading for an integrated energy system and electric vehicle charging stations: A Nash bargaining game approach," Renewable Energy, Elsevier, vol. 155(C), pages 513-530.
    • Handle: RePEc:eee:renene:v:155:y:2020:i:c:p:513-530
    DOI: 10.1016/j.renene.2020.03.006
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    10. Costa, Vinicius Braga Ferreira da & Bonatto, Benedito Donizeti, 2023. "Cutting-edge public policy proposal to maximize the long-term benefits of distributed energy resources," Renewable Energy, Elsevier, vol. 203(C), pages 357-372.
    11. Filipe Bandeiras & Álvaro Gomes & Mário Gomes & Paulo Coelho, 2023. "Exploring Energy Trading Markets in Smart Grid and Microgrid Systems and Their Implications for Sustainability in Smart Cities," Energies, MDPI, vol. 16(2), pages 1-41, January.
    12. Xu, Jiazhu & Yi, Yuqin, 2023. "Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach," Energy, Elsevier, vol. 263(PB).
    13. Zhu, Xu & Sun, Yuanzhang & Yang, Jun & Dou, Zhenlan & Li, Gaojunjie & Xu, Chengying & Wen, Yuxin, 2022. "Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses," Energy, Elsevier, vol. 251(C).
    14. Zhao, Bingxu & Cao, Xiaodong & Duan, Pengfei, 2024. "Cooperative operation of multiple low-carbon microgrids: An optimization study addressing gaming fraud and multiple uncertainties," Energy, Elsevier, vol. 297(C).
    15. Xiang, Liu, 2022. "A large-scale equilibrium model of energy emergency production: Embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors," Energy, Elsevier, vol. 259(C).
    16. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Nash bargaining based collaborative energy management for regional integrated energy systems in uncertain electricity markets," Energy, Elsevier, vol. 269(C).
    17. Panah, Payam Ghaebi & Bornapour, Mosayeb & Hemmati, Reza & Guerrero, Josep M., 2021. "Charging station Stochastic Programming for Hydrogen/Battery Electric Buses using Multi-Criteria Crow Search Algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    18. Zhang, Kaoshe & Gao, Congchong & Zhang, Gang & Xie, Tuo & Li, Hua, 2024. "Electricity and heat sharing strategy of regional comprehensive energy multi-microgrid based on double-layer game," Energy, Elsevier, vol. 293(C).
    19. Chen, Yang & Park, Byungkwon & Kou, Xiao & Hu, Mengqi & Dong, Jin & Li, Fangxing & Amasyali, Kadir & Olama, Mohammed, 2020. "A comparison study on trading behavior and profit distribution in local energy transaction games," Applied Energy, Elsevier, vol. 280(C).
    20. Wang, Zhuo & Hou, Hui & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Xie, Changjun, 2024. "Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach," Applied Energy, Elsevier, vol. 357(C).
    21. Lin, Chun-Cheng & Wu, Yi-Fang & Liu, Wan-Yu, 2021. "Optimal sharing energy of a complex of houses through energy trading in the Internet of energy," Energy, Elsevier, vol. 220(C).

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